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The quantity theory of money was associated with the politics of the right in the '70s and '80s, but a century earlier, particularly in America, it had played an important part in the proposals of the Progressive left. These political associations are examined, and it is argued that a crucial factor in the quantity theory's apparent migration from left to right was its newly-forged links to the case for monetary policy rules in the interwar years. It was also in the 1930s that the endogenous-money Banking School doctrines deployed by anti-quantity theorists during the monetarist controversy first became mainly associated with the case for discretionary policy, rather than with support for the gold-standard rule, as they had been in earlier times. More generally, shifting scientific opinion about the causes of the Great Depression moved the political centre of gravity of monetary economics to the left from the 1930s onwards, and then back towards the right beginning in the '60s, creating an illusion of movement on the quantity theory's part.

Research reveals that liberals and conservatives in the United States diverge about their beliefs regarding climate change. We show empirically that political affiliation also matters with respect to climate related risks such as flooding from hurricanes. Our study is based on a survey conducted 6 months after Superstorm Sandy in 2012 of over 1,000 residents in flood-prone areas in New York City. Democrats' perception of their probability of suffering flood damage is significantly higher than Republicans' and they are also more likely to invest in individual flood protection measures. However, 50% more Democrats than Republicans in our sample expect to receive federal disaster relief after a major flood. These results highlight the importance of taking into account value-based considerations in designing disaster risk management policies.

This study investigated attitudes to the poor as measured by MacDonald's Poverty Scale and the Protestant Ethic Scale. Supporters of the British Conservative Party were found to have more negative attitudes to the poor, and were more likely to blame the poor for their fate than
supporters of the British Labour Party. Supporters of the Liberal/SDP Alliance occupied position between the other two parties on this issue. These results accord with others found in India regarding attribution of the causes of poverty and political ideology.

Partisanship and news source attribution may influence perceived media bias and trust in COVID-19 information. This study aimed to explore how the similarity liking effect relates to political identity and perceived credibility. Our main hypothesis was that individuals would find news sources that shared their political affiliation as more trustworthy than other news sources. To test our hypothesis, 216 undergraduate students participated in a study that measured participants' trustworthiness of a news article with randomized source attribution from a liberal, conservative, and neutral source. A 2X3 ANOVA tested the relationship between our participants' political affiliation, measured by the 12-item Social and Economic Conservatism Scale (Everett, 2013), the news sources' political affiliation, and trustworthiness rating given to the articles by the participants. The results of the ANOVA were not significant; therefore, our data did not support our hypothesis. An exploratory analysis revealed a significant main effect for self-reported affiliation. Specifically, participants who identified as "Other" rated the articles as significantly less trustworthy than Democratic and Republican participants. Our results suggest that there may be other factors that are more prominent in influencing the trustworthiness of news sources apart from political affiliation. Further research is needed to gain an understanding of how social identity theory influences news and media consumption in the United States.

Research reveals that liberals and conservatives in the United States diverge about their beliefs regarding climate change. We show empirically that political affiliation also matters with respect to climate related risks such as flooding from hurricanes. Our study is based on a survey conducted 6 months after Superstorm Sandy in 2012 of over 1,000 residents in flood-prone areas in New York City. Democrats' perception of their probability of suffering flood damage is significantly higher than Republicans' and they are also more likely to invest in individual flood protection measures. However, 50% more Democrats than Republicans in our sample expect to receive federal disaster relief after a major flood. These results highlight the importance of taking into account value-based considerations in designing disaster risk management policies.

Previous literature has found that politically conservative individuals express a lower willingness-to-pay (WTP) for environmental goods than left-wing supporters. Using data from three surveys valuing water we investigate the role of context by evaluating whether the means of provision (public or private) matters. While left-wing voters have higher WTP for publically provided public goods, right-wing voters have a higher WTP when a good is privately provided. Our findings have implications for values typically obtained for environmental public goods using survey data from constructed markets since scenarios typically describe improvements as being publically provided.

Abstract
People often draw inferences about others' underlying characteristics from single and static samples of their appearance, such as facial features, or attractiveness. Evidence also suggests that these judgments occur spontaneously and rapidly. Are humans also able to detect political preferences based on appearance? This article examines to what extent observable lifestyle characteristics influence people's judgments about one's political affiliation and, more importantly, to what degree these judgments are accurate. A conjoint analysis allows for the identification of the specific lifestyle cues that people use to infer one's political affiliation. These results are contrasted with a large and unique dataset (n = 64,745), enabling the assessment of how accurate these cues are. Results suggest that certain lifestyle characteristics, such as type of car or leisure activities, are clearly associated with different political parties, at least in people's minds. Results also suggest that, despite the potential detrimental effects of appearance-based judgments, people are generally pretty good at guessing others' political preferences. This study contributes to a growing body of research on the relationship between lifestyle and political preferences. More generally, it sheds light on the diagnostic value of appearances in everyday social judgments.

Political ideology is one of the most powerful predictors of perceptions about environmental sustainability and related behaviors. The purpose of this study was to investigate how sport fans' sustainability-specific values, perceptions, and norms related to awareness, engagement, and influence of USA collegiate sport sustainability efforts based on political affiliation, accounting for age and gender. Data were collected using an online survey distributed to season ticket holders after the 2019 college football season that featured three sponsored sustainability initiatives at each home game. Multivariate analysis of variance and chi-square difference tests found that self-identified Democrats reported significantly higher pro-environmental values and norms, but sustainability program engagement, sponsored initiatives awareness, and influence of initiatives on behavior were politically neutral. Path analysis found that ascription of responsibility was a significant predictor of sustainability-related engagement and behaviors for both Independents and Republicans. The results and discussion sections highlight how academics and practitioners can account for political affiliation when creating campaign messaging for environmental initiatives.

Discovering user demographic attributes from social media is a problem of considerable interest. The problem setting can be generalized to include three components — users, topics and behaviors. In recent studies on this problem, however, the behavior between users and topics are not effectively incorporated. In our work, we proposed an integrated unsupervised model which takes into consideration all the three components integral to the task. Furthermore, our model incorporates collaborative filtering with probabilistic matrix factorization to solve the data sparsity problem, a computational challenge common to all such tasks. We evaluated our method on a case study of user political affiliation identification, and compared against state-of-the-art baselines. Our model achieved an accuracy of 70.1% for user party detection task.